Enhancing Cybersecurity Defenses in Healthcare Using AI: A Pivotal Role in Fortifying Digital Health Infrastructure

Abstract

In the digital health infrastructure, cyber threats pose a long-term security concern for healthcare facilities, and the existing security measures are not capable enough to minimize the risks and security challenges in the healthcare systems. The concept of artificial intelligence (AI) is changing the way healthcare delivery, patient data management, and administrative tasks operate. At present, to elevate threat detection and prevention, we are using key mechanisms like machine learning, Natural language processing (NLP), and other data analytics. With the integrative concept of cybersecurity and generative AI, we can also investigate the impact of AI-driven solutions in healthcare systems. This study emphasizes AI's crucial role in reforming our response to escalating threats and explores the interdependent relationship between generative AI and cybersecurity and their applications in healthcare so that healthcare facilities can predict and mitigate emerging risks by integrating AI into existing security infrastructure. A total of five generative AI-based cybersecurity defense mechanisms are proposed in this study that can be used to enhance digital infrastructure security in healthcare. These five mechanisms represent dynamic strategies that could revolutionize defensive capabilities from real-time anomaly detection to combating zero-day exploits. The suggested technical approaches outline the organizational needs and select appropriate AI technologies and continuous improvement. Fast identification of irregularities and established security standards allow for proactive threat mitigation. Moreover, the proposed strategies ensure proactive threat mitigation and robust security standards for real-time anomaly detection to combat zero-day exploits. Hence, GenAI-based robust cybersecurity protocols are essential to protect digital healthcare facilities from determined cyber opponents.

Received: 17 August 2024 | Revised: 6 March 2025 | Accepted: 7 March 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Shahazad Niwazi Qurashi: Conceptualization, Methodology, Investigation, Data curation, Writing – original draft, Supervision, Project administration. Farrukh Sobia: Conceptualization, Data curation, Writing – original draft. Wafa A. Hetany: Writing – review & editing. Hani Sultan: Visualization.